This script compares GSVA scores for hallmark and KEGG pathways between PBTA samples with and without DNA repair gene germline PLP variants

Load packages and set directories

library(tidyverse)
library(tibble)
library(ComplexHeatmap)
library(circlize)

Set filepaths

root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "analyses", "CLK1-splicing-impact-morpholino")
results_dir <- file.path(analysis_dir, "results")
plots_dir <- file.path(analysis_dir, "plots")
source(file.path(root_dir, "figures", "theme_for_plots.R"))

Set file paths

gsva_file <- file.path(results_dir, "expr_collapsed_clk1_ctrl_morpho_hallmark_gsva_scores.tsv")
de_gsva_file <- file.path(results_dir, "expr_collapsed_de_clk1_ctrl_morpho_hallmark_gsva_scores.tsv")
kegg_file <- file.path(results_dir, "expr_collapsed_clk1_ctrl_morpho_kegg_gsva_scores.tsv")
de_kegg_file <- file.path(results_dir, "expr_collapsed_de_clk1_ctrl_morpho_kegg_gsva_scores.tsv")
dna_file <- file.path(results_dir, "expr_collapsed_clk1_ctrl_morpho_dna_repair_gsva_scores.tsv")
<<<<<<< HEAD
de_dna_file <- file.path(results_dir, "expr_collapsed_de_clk1_ctrl_morpho_dna_repair_gsva_scores.tsv")

Create histology file for grouping samples

======= de_dna_file <- file.path(results_dir, "expr_collapsed_de_clk1_ctrl_morpho_dna_repair_gsva_scores.tsv") hall_splice_file <- file.path(results_dir, "expr_splice_clk1_ctrl_morpho_hallmark_gsva_scores.tsv") hall_splice_onco_file <- file.path(results_dir, "expr_splice_onco_clk1_ctrl_morpho_hallmark_gsva_scores.tsv") kegg_splice_file <- file.path(results_dir, "expr_splice_clk1_ctrl_morpho_kegg_gsva_scores.tsv") kegg_splice_onco_file <- file.path(results_dir, "expr_splice_onco_clk1_ctrl_morpho_kegg_gsva_scores.tsv") dna_splice_file <- file.path(results_dir, "expr_splice_clk1_ctrl_morpho_dna_repair_gsva_scores.tsv") dna_splice_onco_file <- file.path(results_dir, "expr_splice_onco_clk1_ctrl_morpho_dna_repair_gsva_scores.tsv") # reduce kegg pathways hallmark_file <- file.path(root_dir, "analyses", "clustering_analysis", "input", "genesets.tsv")

Create histology file for grouping samples

hall_to_keep <- read_tsv(hallmark_file) %>%
  filter(Keep == "Yes")
## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): Geneset, Keep
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
>>>>>>> main 
sample_ids <- c("CTRL1", "CTRL2", "CTRL3", "Treated1", "Treated2", "Treated3")
treatments <- c("Non-targeting Morpholino", 
                "Non-targeting Morpholino", 
                "Non-targeting Morpholino", 
                "CLK1 Exon 4 Morpholino", 
                "CLK1 Exon 4 Morpholino",
                "CLK1 Exon 4 Morpholino")
df <- tibble(sample_id = sample_ids, Treatment = treatments)

# read in scores and de results
<<<<<<< HEAD
hallmark_scores <- read_tsv(gsva_file)
## Rows: 300 Columns: 3
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
hallmark_scores <- read_tsv(gsva_file) %>%
  filter(geneset %in% hall_to_keep$Geneset)
## Rows: 300 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
hallmark_pathways <- unique(hallmark_scores$geneset)

<<<<<<< HEAD
kegg_scores <- read_tsv(kegg_file)
## Rows: 1116 Columns: 3
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
kegg_scores <- read_tsv(kegg_file) 
## Rows: 1116 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
kegg_pathways <- unique(kegg_scores$geneset)

dna_scores <- read_tsv(dna_file)
## Rows: 36 Columns: 3
<<<<<<< HEAD
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dna_pathways <- unique(dna_scores$geneset)

<<<<<<< HEAD
hallmark_scores_de <- read_tsv(de_gsva_file)
## Rows: 300 Columns: 3
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
hallmark_scores_de <- read_tsv(de_gsva_file) %>%
  filter(geneset %in% hall_to_keep$Geneset)
## Rows: 300 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
hallmark_pathways_de <- unique(hallmark_scores_de$geneset)

kegg_scores_de <- read_tsv(de_kegg_file)
## Rows: 1116 Columns: 3
<<<<<<< HEAD
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
kegg_pathways_de <- unique(kegg_scores_de$geneset)

dna_scores_de <- read_tsv(de_dna_file)
## Rows: 36 Columns: 3
<<<<<<< HEAD
## ── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): geneset, sample_id
=======
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
>>>>>>> main
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
<<<<<<< HEAD 
dna_pathways_de <- unique(dna_scores_de$geneset)
======= 
dna_pathways_de <- unique(dna_scores_de$geneset)

# splicing
hallmark_scores_sp <- read_tsv(hall_splice_file) %>%
  filter(geneset %in% hall_to_keep$Geneset)
## Rows: 294 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
hallmark_pathways_sp <- unique(hallmark_scores_sp$geneset)

kegg_scores_sp <- read_tsv(kegg_splice_file)
## Rows: 780 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
kegg_pathways_sp <- unique(kegg_scores_sp$geneset)

dna_scores_sp <- read_tsv(dna_splice_file)
## Rows: 36 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dna_pathways_sp <- unique(dna_scores_sp$geneset)

hallmark_scores_sp_onc <- read_tsv(hall_splice_onco_file) %>%
  filter(geneset %in% hall_to_keep$Geneset)
## Rows: 288 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
hallmark_pathways_sp_onc <- unique(hallmark_scores_sp_onc$geneset)

kegg_scores_sp_onc <- read_tsv(kegg_splice_onco_file)
## Rows: 714 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
kegg_pathways_sp_onc <- unique(kegg_scores_sp_onc$geneset)

dna_scores_sp_onc <- read_tsv(dna_splice_onco_file)
## Rows: 30 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): sample_id, geneset
## dbl (1): gsva_score
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
dna_pathways_sp_onc <- unique(dna_scores_sp_onc$geneset)
>>>>>>> main

Run GSVA score comparisons between samples with and without CLK1 morpholino

# Define repair gene source input directories 
treat_ids <- df %>%
  filter(Treatment == "CLK1 Exon 4 Morpholino") %>%
  pull(sample_id) %>%
  unique()

ctrl_ids <- df %>%
  filter(Treatment == "Non-targeting Morpholino") %>%
  pull(sample_id) %>%
  unique()

define data frame to store GSVA scores, differences between groups, and corresponding test statistic p-values

Assuming you have defined kegg_pathways, hallmark_pathways, dna_pathways lists

And kegg_scores, hallmark_scores, dna_scores data frames with columns ‘geneset’, ‘sample_id’, and ‘gsva_score’

treat_ids and ctrl_ids are vectors containing IDs for treated and control samples

# Lists of score data frames and pathway lists
<<<<<<< HEAD
score_sources <- list(kegg_scores, hallmark_scores, dna_scores, kegg_scores_de, hallmark_scores_de, dna_scores_de)
pathway_sources <- list(kegg_pathways, hallmark_pathways, dna_pathways, kegg_pathways_de, hallmark_pathways_de, dna_pathways_de)
score_names <- c("kegg", "hallmark", "dna_repair", "kegg_de", "hallmark_de", "dna_repair_de") # Names for output files
=======
score_sources <- list(kegg_scores, hallmark_scores, dna_scores, kegg_scores_de, hallmark_scores_de, dna_scores_de,
                      kegg_scores_sp, hallmark_scores_sp, dna_scores_sp, kegg_scores_sp_onc, hallmark_scores_sp_onc, dna_scores_sp_onc)
pathway_sources <- list(kegg_pathways, hallmark_pathways, dna_pathways, kegg_pathways_de, hallmark_pathways_de, dna_pathways_de,
                        kegg_pathways_sp, hallmark_pathways_sp, dna_pathways_sp, kegg_pathways_sp_onc, hallmark_pathways_sp_onc, dna_pathways_sp_onc)
score_names <- c("kegg", "hallmark", "dna_repair", "kegg_de", "hallmark_de", "dna_repair_de",
                 "kegg_sp", "hallmark_sp", "dna_repair_sp", "kegg_sp_onc", "hallmark_sp_onc", "dna_repair_sp_onc") # Names for output files
>>>>>>> main

for (i in 1:length(score_sources)) {
  score_df <- score_sources[[i]]
  pathway_list <- pathway_sources[[i]]
  score_name <- score_names[i]
  
  gsva_res <- data.frame(pathway = pathway_list,
                         treat_score = rep(0, length(pathway_list)),
                         ctrl_score = rep(0, length(pathway_list)),
                         score_diff = rep(0, length(pathway_list)),
                         wilcox_stat = rep(0, length(pathway_list)),
                         pvalue = rep(0, length(pathway_list)))
  
 for (k in 1:length(pathway_list)) {
    treat_scores <- score_df[score_df$geneset == pathway_list[k] & score_df$sample_id %in% treat_ids, ]$gsva_score
    ctrl_scores <- score_df[score_df$geneset == pathway_list[k] & score_df$sample_id %in% ctrl_ids, ]$gsva_score
    
    # Ensure treat_scores and ctrl_scores are numeric vectors
    treat_scores <- as.numeric(treat_scores)
    ctrl_scores <- as.numeric(ctrl_scores)
    
    # Proceed with mean calculation and Wilcoxon test as before
    gsva_res$treat_score[k] <- mean(treat_scores, na.rm = TRUE)
    gsva_res$ctrl_score[k] <- mean(ctrl_scores, na.rm = TRUE)
    gsva_res$score_diff[k] <- gsva_res$treat_score[k] - gsva_res$ctrl_score[k]

    if (length(treat_scores) > 0 && length(ctrl_scores) > 0) {
      test_result <- tryCatch({
        wilcox.test(treat_scores, ctrl_scores, alternative = 'two.sided')
      }, warning = function(w) {
        return(list(statistic = NA, p.value = NA))
      }, error = function(e) {
        return(list(statistic = NA, p.value = NA))
      })
      
      gsva_res$wilcox_stat[k] <- test_result$statistic
      gsva_res$pvalue[k] <- test_result$p.value
    } else {
      gsva_res$wilcox_stat[k] <- NA
      gsva_res$pvalue[k] <- NA
    }
}

  
  # Write to output
  output_filename <- paste0("gsva_score_diff_", score_name, ".tsv")
  write_tsv(gsva_res, file.path(results_dir, output_filename))
}

Plot single samples

# create anno colors
top_annot <- df %>%
  dplyr::select(Treatment) %>%
  as.data.frame()
anno_col <- list(Treatment = c("Non-targeting Morpholino" = "#FFC20A", "CLK1 Exon 4 Morpholino" = "#0C7BDC"))

# Heatmap annotation
top_anno = HeatmapAnnotation(df = top_annot,
                         col = anno_col, show_legend = TRUE)
 
# plot
for (i in 1:length(score_sources)) {
  score_df <- score_sources[[i]] %>%
    mutate(geneset = gsub("HALLMARK_|KEGG_", "", geneset),
           geneset = gsub("_", " ", geneset))

  score_df_summarized <- score_df %>%
    mutate(Treatment = ifelse(grepl("CTRL", sample_id), "Non-targeting Morpholino", "CLK1 Exon 4 Morpholino")) %>%
    group_by(geneset, Treatment) %>%
<<<<<<< HEAD
    summarise(Mean_GSVA_Score = mean(gsva_score, na.rm = TRUE)) %>%
    ungroup() 
  
=======
    summarise(Mean_GSVA_Score = mean(gsva_score, na.rm = TRUE), .groups = 'drop')

>>>>>>> main
# Re-order
ordering_metric <- score_df_summarized %>%
  filter(Treatment == "CLK1 Exon 4 Morpholino") %>%
  arrange(desc(Mean_GSVA_Score)) %>%
  pull(geneset)

# Apply this ordering to the 'geneset' factor
score_df_summarized$geneset <- factor(score_df_summarized$geneset, levels = ordering_metric)

  gsva_barplot <- ggplot(score_df_summarized, aes(x = geneset, y = Mean_GSVA_Score, fill = Treatment)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  theme_Publication() +
  labs(x = "Geneset", y = "Mean GSVA score") +
  scale_fill_manual(values = c("Non-targeting Morpholino" = "#FFC20A", "CLK1 Exon 4 Morpholino" = "#0C7BDC")) +
  coord_flip() +
    theme(legend.position = "top") # Positions the legend at the top

    
  score_name <- score_names[i]
  gsva_scores_mat <- score_df %>%
<<<<<<< HEAD
  spread(sample_id, gsva_score) %>%
  tibble::column_to_rownames("geneset") %>%
  as.matrix()
  
  if (i %in% c(1,4)){
=======
  spread(., sample_id, gsva_score) %>%
  tibble::column_to_rownames("geneset") %>%
  as.matrix()
  
  if (i %in% c(1,4,7,10)){
>>>>>>> main
    height <- 30
    width <- 12
    cell_width <- 3
  }
<<<<<<< HEAD
  else if (i %in% c(2,5)){
    height <- 15
    width <- 8
    cell_width <- 3

  }
  else if (i %in% c(3,6)){
    height <- 3.5
    width <- 5.5
    cell_width <- 3

=======
  else if (i %in% c(2,5,8,11)){
    height <- 10
    width <- 8
    cell_width <- 3
  }
  else if (i %in% c(3,6,9,12)){
    height <- 3.5
    width <- 5.5
    cell_width <- 3
>>>>>>> main
  }
  
heatmap_obj <- Heatmap(
  gsva_scores_mat,
<<<<<<< HEAD
  col = colorRamp2(c(-1, 0, 1), c("blue", "white", "darkorange")),
=======
  col = colorRamp2(c(-.8, 0, .8), c("darkblue", "white", "red")),
>>>>>>> main
  name = "GSVA Scores",
  na_col = "whitesmoke",
  width = unit(cell_width, "cm"),
  show_column_names = FALSE,
  cluster_columns = FALSE,
  row_names_gp = grid::gpar(fontsize = 12),
  top_annotation = top_anno,
  heatmap_legend_param = list(direction = "horizontal"),
)

gsva_heatmap <- draw(heatmap_obj, annotation_legend_side = "top", heatmap_legend_side = "top")


output_filename <- paste0("gsva_heatmap_", score_name, ".pdf")
pdf(file.path(plots_dir, output_filename),  height = height, width = width)
print(gsva_heatmap)
print(gsva_barplot)
dev.off()
}
<<<<<<< HEAD 
## `summarise()` has grouped output by 'geneset'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'geneset'. You can override using the `.groups` argument.

## `summarise()` has grouped output by 'geneset'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'geneset'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'geneset'. You can override using the
## `.groups` argument.

## `summarise()` has grouped output by 'geneset'. You can override using the
## `.groups` argument.

How do these look if we make some barplots?

Print session info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
=======



Print session info


sessionInfo()


## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
>>>>>>> main
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
<<<<<<< HEAD
=======
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
>>>>>>> main
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
<<<<<<< HEAD
##  [1] ggthemes_4.2.4        circlize_0.4.15       ComplexHeatmap_2.14.0
##  [4] lubridate_1.9.2       forcats_1.0.0         stringr_1.5.0        
##  [7] dplyr_1.1.1           purrr_1.0.1           readr_2.1.4          
## [10] tidyr_1.3.0           tibble_3.2.1          ggplot2_3.4.2        
## [13] tidyverse_2.0.0      
## 
## loaded via a namespace (and not attached):
##  [1] png_0.1-7           rprojroot_2.0.3     digest_0.6.31      
##  [4] foreach_1.5.2       utf8_1.2.3          R6_2.5.1           
##  [7] stats4_4.2.3        evaluate_0.20       highr_0.10         
## [10] pillar_1.9.0        GlobalOptions_0.1.2 rlang_1.1.0        
## [13] jquerylib_0.1.4     S4Vectors_0.36.2    GetoptLong_1.0.5   
## [16] rmarkdown_2.21      labeling_0.4.2      bit_4.0.5          
## [19] munsell_0.5.0       compiler_4.2.3      xfun_0.38          
## [22] pkgconfig_2.0.3     BiocGenerics_0.44.0 shape_1.4.6        
## [25] htmltools_0.5.5     tidyselect_1.2.0    IRanges_2.32.0     
## [28] codetools_0.2-19    matrixStats_0.62.0  fansi_1.0.4        
## [31] crayon_1.5.2        tzdb_0.3.0          withr_2.5.0        
## [34] jsonlite_1.8.4      gtable_0.3.3        lifecycle_1.0.3    
## [37] magrittr_2.0.3      scales_1.2.1        cli_3.6.1          
## [40] stringi_1.7.12      vroom_1.6.1         cachem_1.0.7       
## [43] farver_2.1.1        doParallel_1.0.17   bslib_0.4.2        
## [46] generics_0.1.3      vctrs_0.6.2         rjson_0.2.21       
## [49] RColorBrewer_1.1-3  iterators_1.0.14    tools_4.2.3        
## [52] bit64_4.0.5         glue_1.6.2          hms_1.1.3          
## [55] parallel_4.2.3      fastmap_1.1.1       yaml_2.3.7         
## [58] timechange_0.2.0    clue_0.3-61         colorspace_2.1-0   
## [61] cluster_2.1.4       knitr_1.42          sass_0.4.5

=======
##  [1] ggthemes_5.1.0        circlize_0.4.16       ComplexHeatmap_2.20.0
##  [4] lubridate_1.9.3       forcats_1.0.0         stringr_1.5.1        
##  [7] dplyr_1.1.4           purrr_1.0.2           readr_2.1.5          
## [10] tidyr_1.3.1           tibble_3.2.1          ggplot2_3.5.1        
## [13] tidyverse_2.0.0      
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
##  [4] digest_0.6.35       timechange_0.3.0    lifecycle_1.0.4    
##  [7] cluster_2.1.6       magrittr_2.0.3      compiler_4.4.0     
## [10] rlang_1.1.4         sass_0.4.9          tools_4.4.0        
## [13] utf8_1.2.4          yaml_2.3.8          knitr_1.47         
## [16] labeling_0.4.3      bit_4.0.5           RColorBrewer_1.1-3 
## [19] withr_3.0.0         BiocGenerics_0.50.0 stats4_4.4.0       
## [22] fansi_1.0.6         colorspace_2.1-0    scales_1.3.0       
## [25] iterators_1.0.14    cli_3.6.2           rmarkdown_2.27     
## [28] crayon_1.5.2        generics_0.1.3      tzdb_0.4.0         
## [31] rjson_0.2.21        cachem_1.1.0        parallel_4.4.0     
## [34] matrixStats_1.3.0   vctrs_0.6.5         jsonlite_1.8.8     
## [37] IRanges_2.38.0      hms_1.1.3           GetoptLong_1.0.5   
## [40] S4Vectors_0.42.0    bit64_4.0.5         clue_0.3-65        
## [43] foreach_1.5.2       jquerylib_0.1.4     glue_1.7.0         
## [46] codetools_0.2-20    stringi_1.8.4       shape_1.4.6.1      
## [49] gtable_0.3.5        munsell_0.5.1       pillar_1.9.0       
## [52] htmltools_0.5.8.1   R6_2.5.1            doParallel_1.0.17  
## [55] rprojroot_2.0.4     vroom_1.6.5         evaluate_0.24.0    
## [58] highr_0.11          png_0.1-8           bslib_0.7.0        
## [61] Rcpp_1.0.12         xfun_0.44           pkgconfig_2.0.3    
## [64] GlobalOptions_0.1.2
>>>>>>> main